Many image processing applications require classifying image samples regarding whether or not a respective sample comprises optical information on a specific object type. An approach to classifying image samples is mainly based on the colour associated with each of the image samples.
One example of such an image processing application is an image processing application that aims at segmentation of concrete objects of the specific object type depicted in an image. Having segmented the concrete object in the image, the segmented object may be exploited in a subsequent image processing step. For instance, segmenting the concrete object in several images of an image sequence may serve as a basis for tracking the object in the image sequence.
Classifying image samples regarding whether or not they comprise optical information on a specific object type is however in many cases not an easy task and therefore error-prone. For instance, an image may one the one hand comprise samples comprising optical information on the specific object type and on the other hand it may comprise samples comprising information on other object types, i.e. other image content. In most cases, it is not known in advance which other image content is contained in an image on which classifying samples of a set of samples of the image regarding whether or not a respective sample comprises optical information on a specific object type based on classification information is to be performed. This may complicate distinguishing pixels comprising optical information on the specific object type from pixels not comprising optical information on the specific object type. Moreover, varying lighting conditions may cause the specific object type to appear differently in different images, thus affecting optical information on the specific object type comprised in samples of the different images. Another problem is that concrete objects of the specific object type often vary in their appearance. This may further complicate correct classification of image samples.